Final Project
Xi Cao
4/29/2020
Project description In this project, we will deal with some data related to America tuition costs in each state and then find out some correlation between the tuition costs and other relative variables.
- Data import and data scraped 1.(a) Read data from the first data source. As data in this source has already been cleaned, so we do not have to clean them any more.
1.(b) Read data from the second source. Th data read from this source is the tuition fees and living costs by state. This data source need to do some cleaning and change some colnames. We deleted some rows and colnames and renamed the colname names. Export it as a csv file.
1.(c) Read more specific tuition costs from the website of each state by a loop and store these table into a list.
1.(d) Changed the specific data collected from each state to dataframes and select data according to different tuition costs. Export these tables as csv files.
- Diversity_school wrangling In the part, we dealt with the diversity_school table to study the difference of enrollment between different states and categories. First, we group this table by state and category and count the total enrollment of different states and categories. Then we make a barplot and a state_choropleth to show it more specific.
## # A tibble: 10 x 3
## # Groups: state [4]
## state category count
## <chr> <chr> <dbl>
## 1 California Total Minority 1654432
## 2 California Women 1476438
## 3 California Hispanic 949890
## 4 Texas Women 876547
## 5 Texas Total Minority 857045
## 6 California White 799516
## 7 New York Women 731847
## 8 Florida Women 642964
## 9 New York White 613885
## 10 Texas White 598209
## Warning in self$bind(): The following regions were missing and are being set to
## NA: district of columbia
From the table, we can see that, the category of “Total Minority” in California has the highest enrollment, and the second and third rank also come from California, that is “Women” and “Hispanic”. And the fourth and fifth come from Texas which is “Women” and “Total Minority”. Besides, we can learn that from the best 10 enrollment, almost half of them come from the category of “Women”. From the first bar plot, we can see that the best 5 high enrollment states are Carlifornia, Texas, New York, Florida, Illinois and they almost have all categories which means these states have more comprehensive universities. And combined with the third plot, we can see that those states with higher total enrollment are mostly spread at the west and east coast of America where economic development is faster. From the second bar plot, we can see that Women, White, Total MInority, Hispanic, Black have more enrollment than other categories and Women has the highest proportion in all of these categories which is same as we have learned from the table.
Historical_tuition wrangling This table show the historical tuition costs from 1985 to 2017. At this table, we only use constant dollars which is more stable with economic status. Then, we make 3 barplot to show the variation of tuition costs between different unversity types and tuition types.
From the plot, although there are some missing values in time in public and private part, basically, the tuition costs has been increasing continuously from 1985 to 2017 no matter which tuition type is. Among these, private universities have the highest tuition cost, and public universities have the lowest tuition cost. Besides, all these three tuition type have increased through years, but “2 year constant” did not increased much while other two types increased over 10000 dollars through these years.salary_potential wrangling This table shows the estimated salary according to different level of career and we will show that the difference of potential salary and university quality(make_world_better_percent: Percent of alumni who think they are making the world a better place, stem_percent: Percent of student body in STEM) between states. First, do some summarise and cleaning on the table and create two barplots and a state_choropleth which can be used to make a comparison with th eplot in step 2.
## Warning in self$bind(): The following regions were missing and are being set to
## NA: district of columbia
First, from the two barplot, we can see that the estiamted early career pay does not differ greatly among states, it is about 40000 dollars, and the difference of make_world_better_percent between states is also not big, around 55%. But the other two variable have large difference between different states: Carlifornia, Massachusetts, New York and Pennsylvania has the highest estiamted mid-career pay, and correspondingly, these four states also have higher stem percentage compared with other states. This may mean that stem correlated majors need more professors for a university. Besides, combined the next plot with the plot “Total Enrollment by State” in part2, we also know that Carlifornia and New York have more enrollment than other states. This means enrollment and salary may have some connections, but this table only shows the estimated salary, so in the next part, we will show the relationship between enrollment and real tuition income.
5.(a) tuition_cost wrangling This table shows the tuition cost for 2018-2019. First, we change missing values to zero. And then we analyze the tuition cost according to different classification criterion.
## # A tibble: 5 x 5
## # Groups: state [5]
## state degree_length avg_in_state_total avg_out_of_state_total avg_total
## <chr> <chr> <dbl> <dbl> <dbl>
## 1 Virginia 4 Year 24331. 42394. 66726.
## 2 Vermont 4 Year 25012 40924 65936
## 3 Arizona 4 Year 23451 42010 65461
## 4 California 4 Year 23592. 39720. 63312.
## 5 Rhode Island 4 Year 23923 38666. 62590.## # A tibble: 5 x 5
## # Groups: state [5]
## state degree_length avg_in_state_total avg_out_of_state_tot… avg_total
## <chr> <chr> <dbl> <dbl> <dbl>
## 1 Vermont 2 Year 68640 68640 137280
## 2 Rhode Island 4 Year 56793. 56793. 113586.
## 3 Massachusetts 4 Year 55763 55763 111526
## 4 Connecticut 4 Year 55008. 55008. 110015
## 5 Maine 4 Year 51419. 51419. 102837.## # A tibble: 5 x 5
## # Groups: state [5]
## state degree_length avg_in_state_total avg_out_of_state_tot… avg_total
## <chr> <chr> <dbl> <dbl> <dbl>
## 1 Missouri 4 Year 47570 47570 95140
## 2 New York 4 Year 41380. 41380. 82759
## 3 Massachusetts 2 Year 41050 41050 82100
## 4 Texas 4 Year 33624 33624 67248
## 5 Vermont 2 Year 32676 32676 65352
The three tables show the best 5 high tuition cost according to different tuition type. we can see that Vermont always has higher tuition cost in all these three tuition types, and Rhode Island and Massachusetts is close to Vermont in private tuition type. From part4, we also know that Massachusetts has almost highest estimated potential. Besides, we can see that the tuition cost has increased compared with the histirical tuition cost in part3. In this part, we have also created 6 bar plots to compare the difference of in-state and out-of-state under different situations which is similar to what we have done in part3. Overall, the in-state tuition cost is much small than out-of-state tuition cost. In private type, in-state tuition cost is the same as out-of-state tuition cost ehich is quite different from the public universities no matter 4-year or 2-year, but the 4-year tuition cost is still about twice that of 2-year. And in some states, they do not provide 2-year education. And compared wthin “Public” type, we can see that 4-year tuition cost is about twice that of 2-year, 4-year tuition cost is about 35000 dollars, but 2-year tuition cost is only about 12500, much smaller than 4-year.Besides, every state have public universities. For profit type, it’s the same with private type, that is, in-state tuition cost is the same as out-of-state tuition cost no matter what the degree length is. Also, there are some states don’t have universities for profit type.
5.(b) tuition_cost + diversity_school In this part, we join these two tables to find out whether there are some coorelations between university diversity and tuition cost. 
From the plot, we can see that there is no correlation between university diversity and tuition cost, all categories have same tuition cost in each state. But, there is some relationship between total enrollment and tuition cost, for example, Carlifornia, Texas, New York have higher enrollment and correspondingly, these states have higher tuition cost in public and profit type of tuition.
- 2020 tuition cost data wrangling 6.(a) In this part, we will deal with the specific data we collected from each state and comibine them together. We also turn costs to numeric type for easy calculation.
## State Number of Schools In-State Public Tuition Fees
## 3 Alaska 10 7293
## 4 Alabama 97 6931
## 5 Arkansas 90 4877
## 6 American Samoa 1 3950
## 7 Arizona 140 4667
## Out-State Public Tuition Fees Private Tuition Fees On-Campus Living Costs
## 3 18608 12891 13454
## 4 13348 16852 12115
## 5 7743 18518 12023
## 6 4250 0 0
## 7 11342 17964 13820
## Off-Campus Living Costs
## 3 16356
## 4 12092
## 5 12972
## 6 4000
## 7 14597## State In-state Undergraduate Total Out-state Undergraduate Total
## 1 alaska 21822 33137
## 2 alabama 20328 26222
## 3 arkansas 19003 21868
## 4 american samoa 6550 6850
## 5 arizona 21161 28374
## In-state Graduate Total Out-state Graduate Total
## 1 27070 39145
## 2 23198 33250
## 3 21549 27222
## 4 2600 2600
## 5 28452 39308
## In-state Undergraduate Tuition Fee Out-state Undergraduate Tuition Fee
## 1 7293 18608
## 2 6770 12664
## 3 4877 7743
## 4 3950 4250
## 5 5180 12393
## In-state Graduate Tuition Fee Out-state Graduate Tuition Fee
## 1 12541 24616
## 2 9639 19692
## 3 7423 13096
## 4 0 0
## 5 12471 23327
## Undergraduate-room Graduate-room Undergraduate-books Graduate-books
## 1 13454 13454 1074 1074
## 2 12115 12115 1443 1443
## 3 12023 12023 2102 2102
## 4 0 0 2600 2600
## 5 13820 13820 2161 2161these two tables are come from the second data source.
6.(b) Do some analysis with the data we collected from the website and find some connection with the previous analysis.
## Warning: Removed 1 rows containing missing values (geom_bar).
From the first two barplot, we can know that, the cost of private type tuition is still higher than that of public type of tuition, but the difference is smaller in some states. And Vermont has the highest tuition costs both in public and private, which is similar as we have learned from part5.(a). As for living costs, there is slightly difference between on-campus living and off-campus living, Carlifornia and Hawaii have relatively higher off-campus living cost and District of Columbia, Massachusetts and Rhode Island have relatively higher on-campus living. But no matter on-campus or off-campus, it’s seems like these are some relationship between tuition costs and living costs: basically, higher tuition costs may lead to higher living costs, and in these states where mostly are coastal cities, the potential salary would be higher either according to Part4.
From the third barplot, we can see that the COA of graduate study is more higher than that of undergraduate study, and obviously, students who are not resident of this state need to pay more than those who are resident of this state. And from the last plot, we caan see that, those states with higher COA are basically coastal cities where have more international students and the economic development is faster.